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Prostate cancer (PC) is the most frequently diagnosed non-skin cancer in the world. Previous studies have shown that genomic alterations represent the most common mechanism for molecular alterations responsible for the development and progression of PC. This highlights the importance of identifying functional genomic variants for early detection in high-risk PC individuals. Great efforts have been made to identify common protein-coding genetic variations; however, the impact of non-coding variations, including regulatory genetic variants, is not well understood. Identification of these variants and the underlying target genes will be a key step in improving the detection and treatment of PC. To gain an understanding of the functional impact of genetic variants, and in particular, regulatory variants in PC, we developed an integrative pipeline (AGV) that uses whole genome/exome sequences, GWAS SNPs, chromosome conformation capture data, and ChIP-Seq signals to investigate the potential impact of genomic variants on the underlying target genes in PC. We identified 646 putative regulatory variants, of which 30 significantly altered the expression of at least one protein-coding gene. Our analysis of chromatin interactions data (Hi-C) revealed that the 30 putative regulatory variants could affect 131 coding and non-coding genes. Interestingly, our study identified the 131 protein-coding genes that are involved in disease-related pathways, including Reactome and MSigDB, for most of which targeted treatment options are currently available. Notably, our analysis revealed several non-coding RNAs, including RP11-136K7.2 and RAMP2-AS1, as potential enhancer elements of the protein-coding genes CDH12 and EZH1, respectively. Our results provide a comprehensive map of genomic variants in PC and reveal their potential contribution to prostate cancer progression and development.

Here we developed KARAJ, a fast and flexible Linux command-line tool to automate the end-to-end process of querying and downloading a wide range of genomic and transcriptomic sequence data types. The input to KARAJ is a list of PMCIDs or publication URLs or various types of accession numbers to automate four tasks as follows; firstly, it provides a summary list of accessible datasets generated by or used in these scientific articles, enabling users to select appropriate datasets; secondly, KARAJ calculates the size of files that users want to download and confirms the availability of adequate space on the local disk; thirdly, it generates a metadata table containing sample information and the experimental design of the corresponding study; and lastly, it enables users to download supplementary data tables attached to publications. Further, KARAJ provides a parallel downloading framework powered by Aspera connect which reduces the downloading time significantly.

Understanding the regulatory mechanisms of gene expression is a crucial objective in genomics. Although the DNA sequence near the transcription start site (TSS) offers valuable insights, recent methods suggest that analyzing only the surrounding DNA may not suffice to accurately predict gene expression levels. We developed GENet (Gene Expression Network from Histone and Transcription Factor Integration), a novel approach that integrates essential regulatory signals from transcription factors and histone modifications into a graph-based model. GENet extends beyond simple DNA sequence analysis by incorporating additional layers of genetic control, which are vital for determining gene expression. Our method markedly enhances the prediction of mRNA levels compared to previous models that depend solely on DNA sequence data. The results underscore the significance of including comprehensive regulatory information in gene expression studies. GENet emerges as a promising tool for researchers, with potential applications extending from fundamental biological research to the development of medical therapies.

Background Multiple sclerosis (MS) is an autoimmune inflammatory disease of the central nervous system (CNS) that T cells become autoreactive by recognizing CNS antigens. Both innate and adaptive immune systems are involved in the pathogenesis of MS. In recent years, the impact of innate immune cells on MS pathogenesis has received more attention. CD56bright NK cells, as an immunoregulatory subset of NK cells, can increase the production of cytokines that modulate adaptive immune responses, whereas CD56dim NK cells are more active in cytolysis functions. These two main subsets of NK cells may have different effects on the onset or progression of MS. Invariant NKT (iNKT) cells are other immune cells involved in the control of autoimmune diseases; however, variant NKT (vNKT) cells, despite limited information, could play a role in MS remission via an immunoregulatory pathway. Aim We aimed to evaluate the influence of MS therapeutic agents on NK and NKT cells and NK cell subtypes. Materials and Methods The possible mechanism of each MS therapeutic agent has been presented here, focusing on the effects of different disease‐modifying therapies on the number of NK and NKT subtypes. Results Expansion of CD56bright NK cells, reduction in the CD56dim cells, and enhancement in NKT cells are the more important innate immune cells alterations following the disease‐modifying therapies. Conclusion Expansion of CD56bright NK cells or reduction in the CD56dim cells has been associated with a successful response to different treatments in MS. iNKT and vNKT cells could have beneficial effects on MS improving. It seems that they are enhanced due to some of MS drugs, leading to disease improvement. However, a reduction in the number of NKT cells could be due to the adverse effects of some of MS drugs on the bone marrow.